Our Technology
Precision. Speed. Scale.
Defined by Accuracy, Interoperability, Trust
Proprietary small language model, trained on medical data.
Designed for precision, traceability, and validation — not generative output.
Complements existing structured EHR data.
Cross-institution analytics.
Participation in international research networks (e.g. OHDSI/EHDEN).
Reuse across studies and partners.
No raw data transfers.
GDPR-compliant by default.
Enables cross-country studies without central data pooling.
Transparent NLP pipeline (no opaque generative outputs)
Continuous auto re-training & QA
Designed to support regulatory-grade RWE
Enriched Insights
Breast Cancer
Lung Cancer
Multiple Myleoma
CLL
Immuno Onco
ATTR-CM
Heart Failure
MDD
Clinical NLP Features
Small, Purpose-Built Models
We use small, domain-specific models, not general-purpose LLMs.
Focus on clinical relevance rather than just scale — without compromising explainability or control.
- Higher precision & accuracy for clinically nuanced concepts
- Faster processing and lower infrastructure requirements
- Streamlined validation, maintenance, and governance
Auto-retraining approach
Our models are built to be continuously auto-retrained using new and improved annotations and validations, updated terminology, and site-specific language.
- End delivery with guaranteed accuracy without adding months to the delivery project timeline through automation
- Improved model performance as more data becomes available
- No need to rebuild pipelines when clinical practice or language changes
Validation-Driven by Clinical Experts
Human-in-the-loop
High-quality NLP starts with high-quality validations.
Set-up designed to support clinician-validated, study-specific labeling, ensuring extracted data reflects real-world clinical meaning.
- Configurability at the site level
- Expert-reviewed training data
- Direct feedback loop between annotators, clinicians, researchers, and models
Transparent & Auditable NLP
Our NLP is designed for full transparency, no black-box predictions.
Enabling clinical validation, regulatory confidence, and reproducible research across institutions.
- 100% traceability from structured variable to source sentence
- Audit-ready outputs aligned with regulatory and EHDS expectations
- Continuously retrainable models
Flexible Deployment: Cloud or On-Prem
The platform is fully deployable in the cloud, on-premise, or in hybrid setups, ensuring compliance with hospital IT policies, data residency requirements, and security standards.
- Alignment with hospital security and governance frameworks
- Scalable deployments from pilot to international programs
Built for Research-Grade Data
NLP outputs are structured, standardised, and quality-controlled to support RWE studies, multi-center research, and regulatory-grade analytics, including mapping to common data models such as OMOP.
- Harmonised datasets across sites and countries
- Integrated data quality controls
- Faster time from raw text to analysis-ready databases
Evaluating the performance of our cNLP pipeline against two open-source alternatives
Biomedical LLMs Pretrained on Non-EHR Data Underperform in Multilingual Real-World Settings
Published in CORIA-TALN-RJCRI-RECITAL 2025
From Raw Notes to OMOP-CDM
Our powerful yet efficient language models automatically standardize raw clinical notes into the OMOP CDM format, enriching existing structured data and making it instantly ready for research and validation.
Data Quality at the Core
LynxCare’s Sentinel is a robust, OMOP Common Data Model-based system designed to continuously measure, benchmark, and improve EHR-derived datasets for regulatory compliance, collaborative studies, and translational research.
FAQs
Find answers to your questions about our data solutions and services.
A federated approach means data remains at the source hospital under their control. Analysis queries are sent to multiple hospitals, each processes the query locally on their data, and only aggregated results (not patient-level data) are shared back, ensuring privacy and compliance.
We deploy a secure local gateway at hospitals that extracts data from Electronic Health Records and other hospital information systems. Our AI and clinical NLP then processes both structured and unstructured data (including narrative clinical notes) and harmonizes it into OMOP Common Data Model format. Request a demo for more info.
5 quality insurance checks from intake to insight (completeness, accuracy, clinical validation, medical review, benchmark match). Read more about Sentinel in our Knowledge Center and on our Blog.